test_modeling_prophetnet.py

# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team, The Microsoft Research team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import copy
import tempfile
import unittest

from transformers import ProphetNetConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from .test_configuration_common import ConfigTester
from .test_generation_utils import GenerationTesterMixin
from .test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor


if is_torch_available():
    import torch

    from transformers import (
        ProphetNetDecoder,
        ProphetNetEncoder,
        ProphetNetForCausalLM,
        ProphetNetForConditionalGeneration,
        ProphetNetModel,
        ProphetNetTokenizer,
    )
    from transformers.modeling_outputs import BaseModelOutput


class ProphetNetModelTester:
    def __init__(
        self,
        parent,
        vocab_size=99,
        batch_size=13,
        hidden_size=16,
        encoder_seq_length=7,
        decoder_seq_length=9,
        # For common tests
        is_training=True,
        use_attention_mask=True,
        use_labels=True,
        decoder_start_token_id=0,
        encoder_ffn_dim=32,
        num_encoder_layers=4,
        num_encoder_attention_heads=4,
        decoder_ffn_dim=32,
        num_decoder_layers=4,
        num_decoder_attention_heads=4,
        max_position_embeddings=30,
        is_encoder_decoder=True,
        pad_token_id=0,
        bos_token_id=1,
        eos_token_id=2,
        ngram=2,
        num_buckets=32,
        relative_max_distance=128,
        disable_ngram_loss=False,
        scope=None,
    ):

        self.parent = parent
        self.batch_size = batch_size
        self.encoder_seq_length = encoder_seq_length
        self.decoder_seq_length = decoder_seq_length
        # For common tests
        self.seq_length = self.decoder_seq_length
        self.is_training = is_training
        self.use_attention_mask = use_attention_mask
        self.use_labels = use_labels

        self.vocab_size = vocab_size
        self.hidden_size = hidden_size
        self.num_hidden_layers = num_decoder_layers
        self.num_encoder_layers = num_encoder_layers
        self.num_decoder_layers = num_decoder_layers
        self.decoder_ffn_dim = decoder_ffn_dim
        self.encoder_ffn_dim = encoder_ffn_dim
        self.num_attention_heads = num_decoder_attention_heads
        self.num_encoder_attention_heads = num_encoder_attention_heads
        self.num_decoder_attention_heads = num_decoder_attention_heads
        self.eos_token_id = eos_token_id
        self.bos_token_id = bos_token_id
        self.pad_token_id = pad_token_id
        self.decoder_start_token_id = decoder_start_token_id
        self.ngram = ngram
        self.num_buckets = num_buckets
        self.relative_max_distance = relative_max_distance
        self.disable_ngram_loss = disable_ngram_loss
        self.max_position_embeddings = max_position_embeddings
        self.is_encoder_decoder = is_encoder_decoder

        self.scope = None
        self.decoder_key_length = decoder_seq_length
        self.base_model_out_len = 7
        self.num_hidden_states_types = 3  # encoder, decoder_main, decoder_ngram
        self.decoder_attention_idx = 2

    def prepare_config_and_inputs(self):
        input_ids = ids_tensor([self.batch_size, self.encoder_seq_length], self.vocab_size)
        decoder_input_ids = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size)

        attention_mask = None
        decoder_attention_mask = None
        if self.use_attention_mask:
            attention_mask = ids_tensor([self.batch_size, self.encoder_seq_length], vocab_size=2)
            decoder_attention_mask = ids_tensor([self.batch_size, self.decoder_seq_length], vocab_size=2)

        lm_labels = None
        if self.use_labels:
            lm_labels = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size)

        config = self.get_config()

        return (
            config,
            input_ids,
            decoder_input_ids,
            attention_mask,
            decoder_attention_mask,
            lm_labels,
        )

    def get_config(self):
        return ProphetNetConfig(
            vocab_size=self.vocab_size,
            hidden_size=self.hidden_size,
            num_encoder_layers=self.num_encoder_layers,
            num_decoder_layers=self.num_decoder_layers,
            decoder_ffn_dim=self.decoder_ffn_dim,
            encoder_ffn_dim=self.encoder_ffn_dim,
            num_encoder_attention_heads=self.num_encoder_attention_heads,
            num_decoder_attention_heads=self.num_decoder_attention_heads,
            eos_token_id=self.eos_token_id,
            bos_token_id=self.bos_token_id,
            pad_token_id=self.pad_token_id,
            decoder_start_token_id=self.decoder_start_token_id,
            ngram=self.ngram,
            num_buckets=self.num_buckets,
            relative_max_distance=self.relative_max_distance,
            disable_ngram_loss=self.disable_ngram_loss,
            max_position_embeddings=self.max_position_embeddings,
            is_encoder_decoder=self.is_encoder_decoder,
        )

    def prepare_config_and_inputs_for_decoder(self):
        (
            config,
            input_ids,
            decoder_input_ids,
            attention_mask,
            decoder_attention_mask,
            lm_labels,
        ) = self.prepare_config_and_inputs()

        encoder_hidden_states = floats_tensor([self.batch_size, self.encoder_seq_length, self.hidden_size])
        encoder_attention_mask = ids_tensor([self.batch_size, self.encoder_seq_length], vocab_size=2)

        return (
            config,
            decoder_input_ids,
            decoder_attention_mask,
            encoder_hidden_states,
            encoder_attention_mask,
            lm_labels,
        )

    def check_prepare_lm_labels_via_shift_left(
        self,
        config,
        input_ids,
        decoder_input_ids,
        attention_mask,
        decoder_attention_mask,
        lm_labels,
    ):
        model = ProphetNetModel(config=config)
        model.to(torch_device)
        model.eval()

        # make sure that lm_labels are correctly padded from the right
        lm_labels.masked_fill_((lm_labels == self.decoder_start_token_id), self.eos_token_id)

        # add casaul pad token mask
        triangular_mask = torch.tril(lm_labels.new_ones(lm_labels.shape)).logical_not()
        lm_labels.masked_fill_(triangular_mask, self.pad_token_id)
        decoder_input_ids = model._shift_right(lm_labels)

        for i, (decoder_input_ids_slice, lm_labels_slice) in enumerate(zip(decoder_input_ids, lm_labels)):
            # first item
            self.parent.assertEqual(decoder_input_ids_slice[0].item(), self.decoder_start_token_id)
            if i < decoder_input_ids_slice.shape[-1]:
                if i < decoder_input_ids.shape[-1] - 1:
                    # items before diagonal
                    self.parent.assertListEqual(
                        decoder_input_ids_slice[1 : i + 1].tolist(), lm_labels_slice[:i].tolist()
                    )
                # pad items after diagonal
                if i < decoder_input_ids.shape[-1] - 2:
                    self.parent.assertListEqual(
                        decoder_input_ids_slice[i + 2 :].tolist(), lm_labels_slice[i + 1 : -1].tolist()
                    )
            else:
                # all items after square
                self.parent.assertListEqual(decoder_input_ids_slice[1:].tolist(), lm_labels_slice[:-1].tolist())

    def create_and_check_model(
        self,
        config,
        input_ids,
        decoder_input_ids,
        attention_mask,
        decoder_attention_mask,
        lm_labels,
    ):
        model = ProphetNetModel(config=config)
        model.to(torch_device)
        model.eval()
        result = model(
            input_ids=input_ids,
            decoder_input_ids=decoder_input_ids,
            attention_mask=attention_mask,
            decoder_attention_mask=decoder_attention_mask,
        )
        result = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids)
        decoder_output = result.last_hidden_state
        decoder_past = result.past_key_values
        encoder_output = result.encoder_last_hidden_state

        self.parent.assertEqual(encoder_output.size(), (self.batch_size, self.encoder_seq_length, self.hidden_size))
        self.parent.assertEqual(decoder_output.size(), (self.batch_size, self.decoder_seq_length, self.hidden_size))
        # There should be `num_layers` key value embeddings stored in decoder_past
        self.parent.assertEqual(len(decoder_past), config.num_decoder_layers)
        # There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
        self.parent.assertEqual(len(decoder_past[0]), 4)  # cross-attention + uni-directional self-attention

    def create_and_check_with_lm_head(
        self,
        config,
        input_ids,
        decoder_input_ids,
        attention_mask,
        decoder_attention_mask,
        lm_labels,
    ):
        model = ProphetNetForConditionalGeneration(config=config).to(torch_device).eval()
        outputs = model(
            input_ids=input_ids,
            decoder_input_ids=decoder_input_ids,
            decoder_attention_mask=decoder_attention_mask,
            labels=lm_labels,
        )
        self.parent.assertEqual(len(outputs), 5)
        self.parent.assertEqual(outputs["logits"].size(), (self.batch_size, self.decoder_seq_length, self.vocab_size))
        self.parent.assertEqual(outputs["loss"].size(), ())

    def create_and_check_causal_lm_decoder(
        self,
        config,
        input_ids,
        decoder_input_ids,
        attention_mask,
        decoder_attention_mask,
        lm_labels,
    ):
        model = ProphetNetForCausalLM(config=config).to(torch_device).eval()
        outputs = model(
            input_ids=decoder_input_ids,
            attention_mask=decoder_attention_mask,
            labels=lm_labels,
        )
        self.parent.assertEqual(len(outputs), 4)
        self.parent.assertEqual(outputs["logits"].size(), (self.batch_size, self.decoder_seq_length, self.vocab_size))
        self.parent.assertEqual(outputs["loss"].size(), ())

    def create_and_check_generate_with_past_key_value_states(
        self,
        config,
        input_ids,
        decoder_input_ids,
        attention_mask,
        decoder_attention_mask,
        lm_labels,
    ):
        model = ProphetNetForConditionalGeneration(config=config).to(torch_device).eval()
        torch.manual_seed(0)
        output_without_past_cache = model.generate(
            input_ids[:1], num_beams=2, max_length=5, do_sample=True, use_cache=False
        )
        torch.manual_seed(0)
        output_with_past_cache = model.generate(input_ids[:1], num_beams=2, max_length=5, do_sample=True)
        self.parent.assertTrue(torch.all(output_with_past_cache == output_without_past_cache))

    def create_and_check_decoder_generate_with_past_key_value_states(
        self,
        config,
        input_ids,
        decoder_input_ids,
        attention_mask,
        decoder_attention_mask,
        lm_labels,
    ):
        model = ProphetNetForCausalLM(config=config).to(torch_device).eval()
        torch.manual_seed(0)
        output_without_past_cache = model.generate(
            input_ids[:1], num_beams=2, max_length=10, do_sample=True, use_cache=False
        )
        torch.manual_seed(0)
        output_with_past_cache = model.generate(input_ids[:1], num_beams=2, max_length=10, do_sample=True)
        self.parent.assertTrue(torch.all(output_with_past_cache == output_without_past_cache))

    def create_and_check_model_fp16_forward(
        self,
        config,
        input_ids,
        decoder_input_ids,
        attention_mask,
        decoder_attention_mask,
        lm_labels,
    ):
        model = ProphetNetModel(config=config).to(torch_device).half().eval()
        output = model(input_ids, decoder_input_ids=input_ids, attention_mask=attention_mask)["last_hidden_state"]
        self.parent.assertFalse(torch.isnan(output).any().item())

    def create_and_check_encoder_decoder_shared_weights(
        self,
        config,
        input_ids,
        decoder_input_ids,
        attention_mask,
        decoder_attention_mask,
        lm_labels,
    ):
        for model_class in [ProphetNetModel, ProphetNetForConditionalGeneration]:
            torch.manual_seed(0)
            model = model_class(config=config).to(torch_device).eval()
            # load state dict copies weights but does not tie them

            if model_class == ProphetNetForConditionalGeneration:
                model.prophetnet.encoder.load_state_dict(model.prophetnet.decoder.state_dict(), strict=False)
            else:
                model.encoder.load_state_dict(model.decoder.state_dict(), strict=False)

            torch.manual_seed(0)
            tied_config = copy.deepcopy(config)
            tied_config.tie_encoder_decoder = True
            tied_model = model_class(config=tied_config).to(torch_device).eval()

            model_result = model(
                input_ids=input_ids,
                decoder_input_ids=decoder_input_ids,
                attention_mask=attention_mask,
                decoder_attention_mask=decoder_attention_mask,
            )

            tied_model_result = tied_model(
                input_ids=input_ids,
                decoder_input_ids=decoder_input_ids,
                attention_mask=attention_mask,
                decoder_attention_mask=decoder_attention_mask,
            )

            # check that models has less parameters
            self.parent.assertLess(
                sum(p.numel() for p in tied_model.parameters()), sum(p.numel() for p in model.parameters())
            )
            random_slice_idx = ids_tensor((1,), model_result[0].shape[-1]).item()

            # check that outputs are equal
            self.parent.assertTrue(
                torch.allclose(
                    model_result[0][0, :, random_slice_idx], tied_model_result[0][0, :, random_slice_idx], atol=1e-4
                )
            )

            # check that outputs after saving and loading are equal
            with tempfile.TemporaryDirectory() as tmpdirname:
                tied_model.save_pretrained(tmpdirname)
                tied_model = model_class.from_pretrained(tmpdirname)
                tied_model.to(torch_device)
                tied_model.eval()

                # check that models has less parameters
                self.parent.assertLess(
                    sum(p.numel() for p in tied_model.parameters()), sum(p.numel() for p in model.parameters())
                )
                random_slice_idx = ids_tensor((1,), model_result[0].shape[-1]).item()

                tied_model_result = tied_model(
                    input_ids=input_ids,
                    decoder_input_ids=decoder_input_ids,
                    attention_mask=attention_mask,
                    decoder_attention_mask=decoder_attention_mask,
                )

                # check that outputs are equal
                self.parent.assertTrue(
                    torch.allclose(
                        model_result[0][0, :, random_slice_idx],
                        tied_model_result[0][0, :, random_slice_idx],
                        atol=1e-4,
                    )
                )

    def check_fast_integration(
        self,
        config,
        *args,
    ):
        input_ids = torch.tensor([[7, 4, 78, 0, 24, 52, 43]], device=torch_device, dtype=torch.long)
        decoder_input_ids = torch.tensor([[12, 62, 25, 11, 47, 15, 14]], device=torch_device, dtype=torch.long)
        attention_mask = torch.tensor([[1, 1, 1, 0, 1, 0, 0]], device=torch_device, dtype=torch.long)
        decoder_attention_mask = torch.tensor([[1, 1, 1, 0, 0, 1, 0]], device=torch_device, dtype=torch.long)
        lm_labels = torch.tensor([[62, 25, 11, 47, 15, 14, 24]], device=torch_device, dtype=torch.long)
        torch.manual_seed(0)
        config.ngram = 4
        model = ProphetNetForConditionalGeneration(config=config)
        model.to(torch_device)
        model.eval()
        with torch.no_grad():
            result = model(
                input_ids=input_ids,
                decoder_input_ids=decoder_input_ids,
                attention_mask=attention_mask,
                decoder_attention_mask=decoder_attention_mask,
                labels=lm_labels,
            )
        self.parent.assertTrue(torch.allclose(result.loss, torch.tensor(4.5819, device=torch_device), atol=1e-3))

        expected_logit_slice = torch.tensor(
            [-0.1565, 0.0418, 0.1207, 0.0030, 0.0665, 0.0467, 0.0412], device=torch_device
        )
        self.parent.assertTrue(torch.allclose(result.logits[0, :, 1], expected_logit_slice, atol=1e-3))

    def check_model_with_attn_mask(self, config, input_ids, decoder_input_ids, *args):
        model = ProphetNetModel(config=config)
        model.to(torch_device)
        model.eval()

        outputs_no_mask = model(input_ids=input_ids[:, :5], decoder_input_ids=decoder_input_ids[:, :5])
        attention_mask = torch.ones_like(input_ids)
        decoder_attention_mask = torch.ones_like(decoder_input_ids)

        attention_mask[:, 5:] = 0

        outputs_with_mask = model(
            input_ids=input_ids,
            attention_mask=attention_mask,
            decoder_input_ids=decoder_input_ids,
            decoder_attention_mask=decoder_attention_mask,
        )

        # check encoder
        self.parent.assertTrue(
            torch.allclose(
                outputs_no_mask.encoder_last_hidden_state[0, :, 0],
                outputs_with_mask.encoder_last_hidden_state[0, :5, 0],
                atol=1e-3,
            )
        )

        # check decoder
        # main stream
        self.parent.assertTrue(
            torch.allclose(
                outputs_no_mask.last_hidden_state[0, :, 0], outputs_with_mask.last_hidden_state[0, :5, 0], atol=1e-3
            )
        )
        # predict stream
        self.parent.assertTrue(
            torch.allclose(
                outputs_no_mask.last_hidden_state_ngram[0, :5, 0],
                outputs_with_mask.last_hidden_state_ngram[0, :5, 0],
                atol=1e-2,
            )
        )

    def check_causal_lm_from_pretrained(
        self, config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, *args
    ):
        model = ProphetNetForConditionalGeneration(config).to(torch_device).eval()

        with tempfile.TemporaryDirectory() as tmp_dirname:
            model.save_pretrained(tmp_dirname)
            decoder = ProphetNetForCausalLM.from_pretrained(tmp_dirname).to(torch_device)

        encoder_hidden_states = model.prophetnet.encoder(input_ids).last_hidden_state

        model_outputs = model(
            encoder_outputs=BaseModelOutput(last_hidden_state=encoder_hidden_states),
            decoder_input_ids=decoder_input_ids,
        )
        dec_outputs = decoder(encoder_hidden_states=encoder_hidden_states, input_ids=decoder_input_ids)

        self.parent.assertTrue(
            torch.allclose(
                model_outputs.logits[0, :5],
                dec_outputs.logits[0, :5],
                atol=1e-3,
            )
        )

    def prepare_config_and_inputs_for_common(self):
        config_and_inputs = self.prepare_config_and_inputs()
        (
            config,
            input_ids,
            decoder_input_ids,
            attention_mask,
            decoder_attention_mask,
            lm_labels,
        ) = config_and_inputs

        inputs_dict = {
            "input_ids": input_ids,
            "attention_mask": attention_mask,
            "decoder_input_ids": decoder_input_ids,
            "decoder_attention_mask": decoder_attention_mask,
            "use_cache": False,
        }
        return config, inputs_dict


class ProphetNetStandaloneDecoderModelTester:
    def __init__(
        self,
        parent,
        vocab_size=99,
        batch_size=13,
        hidden_size=16,
        encoder_seq_length=7,
        decoder_seq_length=7,
        # For common tests
        is_training=True,
        is_decoder=True,
        use_attention_mask=True,
        add_cross_attention=False,
        use_cache=False,
        use_labels=True,
        decoder_start_token_id=0,
        encoder_ffn_dim=32,
        num_encoder_layers=4,
        num_encoder_attention_heads=4,
        decoder_ffn_dim=32,
        num_decoder_layers=4,
        num_decoder_attention_heads=4,
        max_position_embeddings=30,
        is_encoder_decoder=False,
        pad_token_id=0,
        bos_token_id=1,
        eos_token_id=2,
        ngram=2,
        num_buckets=32,
        relative_max_distance=128,
        disable_ngram_loss=False,
        scope=None,
    ):
        self.parent = parent
        self.batch_size = batch_size
        self.encoder_seq_length = encoder_seq_length
        self.decoder_seq_length = decoder_seq_length
        # For common tests
        self.seq_length = self.decoder_seq_length
        self.is_training = is_training
        self.use_attention_mask = use_attention_mask
        self.use_labels = use_labels

        self.vocab_size = vocab_size
        self.hidden_size = hidden_size
        self.num_hidden_layers = num_decoder_layers
        self.num_encoder_layers = num_encoder_layers
        self.num_decoder_layers = num_decoder_layers
        self.decoder_ffn_dim = decoder_ffn_dim
        self.encoder_ffn_dim = encoder_ffn_dim
        self.num_attention_heads = num_decoder_attention_heads
        self.num_encoder_attention_heads = num_encoder_attention_heads
        self.num_decoder_attention_heads = num_decoder_attention_heads
        self.eos_token_id = eos_token_id
        self.bos_token_id = bos_token_id
        self.pad_token_id = pad_token_id
        self.decoder_start_token_id = decoder_start_token_id
        self.ngram = ngram
        self.num_buckets = num_buckets
        self.relative_max_distance = relative_max_distance
        self.use_cache = use_cache
        self.disable_ngram_loss = disable_ngram_loss
        self.max_position_embeddings = max_position_embeddings
        self.add_cross_attention = add_cross_attention
        self.is_encoder_decoder = is_encoder_decoder

        self.scope = None
        self.decoder_key_length = decoder_seq_length
        self.base_model_out_len = 2
        self.num_hidden_states_types = 2  # decoder_main, decoder_ngram
        self.decoder_attention_idx = 1

    def prepare_config_and_inputs(self):
        input_ids = ids_tensor([self.batch_size, self.encoder_seq_length], self.vocab_size)

        attention_mask = None
        if self.use_attention_mask:
            attention_mask = ids_tensor([self.batch_size, self.encoder_seq_length], vocab_size=2)

        lm_labels = None
        if self.use_labels:
            lm_labels = ids_tensor([self.batch_size, self.encoder_seq_length], self.vocab_size)

        config = ProphetNetConfig(
            vocab_size=self.vocab_size,
            hidden_size=self.hidden_size,
            num_encoder_layers=self.num_encoder_layers,
            num_decoder_layers=self.num_decoder_layers,
            decoder_ffn_dim=self.decoder_ffn_dim,
            encoder_ffn_dim=self.encoder_ffn_dim,
            num_encoder_attention_heads=self.num_encoder_attention_heads,
            num_decoder_attention_heads=self.num_decoder_attention_heads,
            eos_token_id=self.eos_token_id,
            bos_token_id=self.bos_token_id,
            use_cache=self.use_cache,
            pad_token_id=self.pad_token_id,
            decoder_start_token_id=self.decoder_start_token_id,
            ngram=self.ngram,
            num_buckets=self.num_buckets,
            relative_max_distance=self.relative_max_distance,
            disable_ngram_loss=self.disable_ngram_loss,
            max_position_embeddings=self.max_position_embeddings,
            add_cross_attention=self.add_cross_attention,
            is_encoder_decoder=self.is_encoder_decoder,
        )

        return (
            config,
            input_ids,
            attention_mask,
            lm_labels,
        )

    def prepare_config_and_inputs_for_decoder(self):
        (
            config,
            input_ids,
            attention_mask,
            lm_labels,
        ) = self.prepare_config_and_inputs()

        encoder_hidden_states = floats_tensor([self.batch_size, self.encoder_seq_length, self.hidden_size])
        encoder_attention_mask = ids_tensor([self.batch_size, self.encoder_seq_length], vocab_size=2)

        return (
            config,
            input_ids,
            attention_mask,
            encoder_hidden_states,
            encoder_attention_mask,
            lm_labels,
        )

    def create_and_check_decoder_model_past(
        self,
        config,
        input_ids,
        attention_mask,
        lm_labels,
    ):
        config.use_cache = True
        model = ProphetNetDecoder(config=config).to(torch_device).eval()
        # first forward pass
        outputs = model(input_ids, use_cache=True)
        outputs_use_cache_conf = model(input_ids)
        outputs_no_past = model(input_ids, use_cache=False)

        self.parent.assertTrue(len(outputs) == len(outputs_use_cache_conf))
        self.parent.assertTrue(len(outputs) == len(outputs_no_past) + 1)

        past_key_values = outputs["past_key_values"]

        # create hypothetical next token and extent to next_input_ids
        next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)

        # append to next input_ids and
        next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)

        output_from_no_past = model(next_input_ids)["last_hidden_state"]
        output_from_past = model(next_tokens, past_key_values=past_key_values)["last_hidden_state"]

        # select random slice
        random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
        output_from_no_past_slice = output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
        output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach()

        # test that outputs are equal for slice
        assert torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3)

    def create_and_check_decoder_model_attention_mask_past(
        self,
        config,
        input_ids,
        attention_mask,
        lm_labels,
    ):
        model = ProphetNetDecoder(config=config).to(torch_device).eval()

        # create attention mask
        attn_mask = torch.ones(input_ids.shape, dtype=torch.long, device=torch_device)

        half_seq_length = input_ids.shape[-1] // 2
        attn_mask[:, half_seq_length:] = 0

        # first forward pass
        past_key_values = model(input_ids, attention_mask=attn_mask, use_cache=True)["past_key_values"]

        # create hypothetical next token and extent to next_input_ids
        next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)

        # change a random masked slice from input_ids
        random_seq_idx_to_change = ids_tensor((1,), half_seq_length).item() + 1
        random_other_next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size).squeeze(-1)
        input_ids[:, -random_seq_idx_to_change] = random_other_next_tokens

        # append to next input_ids and attn_mask
        next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
        attn_mask = torch.cat(
            [attn_mask, torch.ones((attn_mask.shape[0], 1), dtype=torch.long, device=torch_device)],
            dim=1,
        )

        # get two different outputs
        output_from_no_past = model(next_input_ids)["last_hidden_state"]
        output_from_past = model(next_tokens, past_key_values=past_key_values)["last_hidden_state"]

        # select random slice
        random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
        output_from_no_past_slice = output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
        output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach()

        # test that outputs are equal for slice
        assert torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-2)

    def prepare_config_and_inputs_for_common(self):
        config_and_inputs = self.prepare_config_and_inputs()
        (
            config,
            input_ids,
            attention_mask,
            lm_labels,
        ) = config_and_inputs

        inputs_dict = {
            "input_ids": input_ids,
            "attention_mask": attention_mask,
        }
        return config, inputs_dict


class ProphetNetStandaloneEncoderModelTester:
    def __init__(
        self,
        parent,
        vocab_size=99,
        batch_size=13,
        hidden_size=16,
        encoder_seq_length=7,
        decoder_seq_length=7,
        # For common tests
        is_training=True,
        is_decoder=False,
        use_attention_mask=True,
        add_cross_attention=False,
        use_cache=False,
        use_labels=True,
        decoder_start_token_id=0,
        encoder_ffn_dim=32,
        num_encoder_layers=4,
        num_encoder_attention_heads=4,
        decoder_ffn_dim=32,
        num_decoder_layers=4,
        num_decoder_attention_heads=4,
        max_position_embeddings=30,
        is_encoder_decoder=False,
        pad_token_id=0,
        bos_token_id=1,
        eos_token_id=2,
        num_buckets=32,
        relative_max_distance=128,
        disable_ngram_loss=False,
        scope=None,
    ):
        self.parent = parent
        self.batch_size = batch_size
        self.encoder_seq_length = encoder_seq_length
        self.decoder_seq_length = decoder_seq_length
        # For common tests
        self.seq_length = self.decoder_seq_length
        self.is_training = is_training
        self.use_attention_mask = use_attention_mask
        self.use_labels = use_labels

        self.vocab_size = vocab_size
        self.hidden_size = hidden_size
        self.num_hidden_layers = num_decoder_layers
        self.num_encoder_layers = num_encoder_layers
        self.num_decoder_layers = num_decoder_layers
        self.decoder_ffn_dim = decoder_ffn_dim
        self.encoder_ffn_dim = encoder_ffn_dim
        self.num_attention_heads = num_decoder_attention_heads
        self.num_encoder_attention_heads = num_encoder_attention_heads
        self.num_decoder_attention_heads = num_decoder_attention_heads
        self.eos_token_id = eos_token_id
        self.bos_token_id = bos_token_id
        self.pad_token_id = pad_token_id
        self.decoder_start_token_id = decoder_start_token_id
        self.num_buckets = num_buckets
        self.relative_max_distance = relative_max_distance
        self.use_cache = use_cache
        self.disable_ngram_loss = disable_ngram_loss
        self.max_position_embeddings = max_position_embeddings
        self.add_cross_attention = add_cross_attention
        self.is_encoder_decoder = is_encoder_decoder

        self.scope = None
        self.decoder_key_length = decoder_seq_length
        self.base_model_out_len = 1
        self.num_hidden_states_types = 1
        self.decoder_attention_idx = 1

    def prepare_config_and_inputs(self):
        input_ids = ids_tensor([self.batch_size, self.encoder_seq_length], self.vocab_size)

        attention_mask = None
        if self.use_attention_mask:
            attention_mask = ids_tensor([self.batch_size, self.encoder_seq_length], vocab_size=2)

        config = ProphetNetConfig(
            vocab_size=self.vocab_size,
            hidden_size=self.hidden_size,
            num_encoder_layers=self.num_encoder_layers,
            num_decoder_layers=self.num_decoder_layers,
            decoder_ffn_dim=self.decoder_ffn_dim,
            encoder_ffn_dim=self.encoder_ffn_dim,
            num_encoder_attention_heads=self.num_encoder_attention_heads,
            num_decoder_attention_heads=self.num_decoder_attention_heads,
            eos_token_id=self.eos_token_id,
            bos_token_id=self.bos_token_id,
            use_cache=self.use_cache,
            pad_token_id=self.pad_token_id,
            decoder_start_token_id=self.decoder_start_token_id,
            num_buckets=self.num_buckets,
            relative_max_distance=self.relative_max_distance,
            disable_ngram_loss=self.disable_ngram_loss,
            max_position_embeddings=self.max_position_embeddings,
            add_cross_attention=self.add_cross_attention,
            is_encoder_decoder=self.is_encoder_decoder,
        )

        return (
            config,
            input_ids,
            attention_mask,
        )

    def prepare_config_and_inputs_for_common(self):
        config_and_inputs = self.prepare_config_and_inputs()
        (
            config,
            input_ids,
            attention_mask,
        ) = config_and_inputs

        inputs_dict = {
            "input_ids": input_ids,
            "attention_mask": attention_mask,
        }
        return config, inputs_dict


@require_torch
class ProphetNetModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
    all_model_classes = (ProphetNetModel, ProphetNetForConditionalGeneration) if is_torch_available() else ()
    all_generative_model_classes = (ProphetNetForConditionalGeneration,) if is_torch_available() else ()
    test_pruning = False
    test_torchscript = False
    test_resize_embeddings = False
    is_encoder_decoder = True

    def setUp(self):
        self.model_tester = ProphetNetModelTester(self)
        self.config_tester = ConfigTester(self, config_class=ProphetNetConfig)

    def test_config(self):
        self.config_tester.run_common_tests()

    def test_model(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*config_and_inputs)

    def test_lm_model(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_with_lm_head(*config_and_inputs)

    def test_only_decoder_causal_model(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_causal_lm_decoder(*config_and_inputs)

    def test_fast_integration(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.check_fast_integration(*config_and_inputs)

    def test_shared_weights(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_encoder_decoder_shared_weights(*config_and_inputs)

    def test_shift_labels_via_shift_left(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.check_prepare_lm_labels_via_shift_left(*config_and_inputs)

    @unittest.skip("Flaky test with no simple resolution. TODO Fix me @patrickvonplaten")
    def test_decoder_model_generate(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_generate_with_past_key_value_states(*config_and_inputs)

    def test_encoder_decoder_model_generate(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_decoder_generate_with_past_key_value_states(*config_and_inputs)

    def test_attn_mask_model(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.check_model_with_attn_mask(*config_and_inputs)

    def test_config_save(self):
        config = self.model_tester.prepare_config_and_inputs()[0]
        config.add_cross_attention = False
        with tempfile.TemporaryDirectory() as tmp_dirname:
            config.save_pretrained(tmp_dirname)
            config = ProphetNetConfig.from_pretrained(tmp_dirname)

        self.assertFalse(config.add_cross_attention)

    def test_causal_lm_from_pretrained(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.check_causal_lm_from_pretrained(*config_and_inputs)

    @unittest.skipIf(torch_device == "cpu", "Cant do half precision")
    def test_fp16_forward(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model_fp16_forward(*config_and_inputs)

    # methods overwrite method in `test_modeling_common.py`
    def test_attention_outputs(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        seq_len = getattr(self.model_tester, "seq_length", None)
        decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", seq_len)
        encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", seq_len)
        decoder_key_length = getattr(self.model_tester, "decoder_key_length", decoder_seq_length)
        encoder_key_length = getattr(self.model_tester, "key_length", encoder_seq_length)
        chunk_length = getattr(self.model_tester, "chunk_length", None)
        if chunk_length is not None and hasattr(self.model_tester, "num_hashes"):
            encoder_seq_length = encoder_seq_length * self.model_tester.num_hashes

        for model_class in self.all_model_classes:
            inputs_dict["output_attentions"] = True
            inputs_dict["output_hidden_states"] = False
            model = model_class(config)
            model.to(torch_device)
            model.eval()
            with torch.no_grad():
                outputs = model(**self._prepare_for_class(inputs_dict, model_class))
            attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
            self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)

            # check that output_attentions also work using config
            del inputs_dict["output_attentions"]
            config.output_attentions = True
            model = model_class(config)
            model.to(torch_device)
            model.eval()
            with torch.no_grad():
                outputs = model(**self._prepare_for_class(inputs_dict, model_class))
            attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
            self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)

            if chunk_length is not None:
                self.assertListEqual(
                    list(attentions[0].shape[-4:]),
                    [self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length],
                )
            else:
                self.assertListEqual(
                    list(attentions[0].shape[-3:]),
                    [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
                )
            out_len = len(outputs)

            correct_outlen = 7

            # loss is at first position
            if "labels" in inputs_dict:
                correct_outlen += 1  # loss is added to beginning

            self.assertEqual(out_len, correct_outlen)

            # decoder attentions
            decoder_attentions = outputs.decoder_attentions
            self.assertIsInstance(decoder_attentions, (list, tuple))
            self.assertEqual(len(decoder_attentions), self.model_tester.num_hidden_layers)
            self.assertListEqual(
                list(decoder_attentions[0].shape[-3:]),
                [self.model_tester.num_attention_heads, decoder_seq_length, decoder_key_length],
            )

            # cross attentions
            cross_attentions = outputs.cross_attentions
            self.assertIsInstance(cross_attentions, (list, tuple))
            self.assertEqual(len(cross_attentions), self.model_tester.num_hidden_layers)
            self.assertListEqual(
                list(cross_attentions[0].shape[-3:]),
                [
                    self.model_tester.num_attention_heads,
                    (self.model_tester.ngram + 1) * decoder_seq_length,
                    encoder_key_length,
                ],
            )

            # Check attention is always last and order is fine
            inputs_dict["output_attentions"] = True
            inputs_dict["output_hidden_states"] = True
            model = model_class(config)
            model.to(torch_device)
            model.eval()
            with torch.no_grad():
                outputs = model(**self._prepare_for_class(inputs_dict, model_class))

            if hasattr(self.model_tester, "num_hidden_states_types"):
                added_hidden_states = self.model_tester.num_hidden_states_types
            elif self.is_encoder_decoder:
                added_hidden_states = 2
            else:
                added_hidden_states = 1
            self.assertEqual(out_len + added_hidden_states, len(outputs))

            self_attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions

            self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers)
            if chunk_length is not None:
                self.assertListEqual(
                    list(self_attentions[0].shape[-4:]),
                    [self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length],
                )
            else:
                self.assertListEqual(
                    list(self_attentions[0].shape[-3:]),
                    [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
                )

    def test_retain_grad_hidden_states_attentions(self):
        # decoder cannot keep gradients
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        config.output_hidden_states = True
        config.output_attentions = True

        # no need to test all models as different heads yield the same functionality
        model_class = self.all_model_classes[0]
        model = model_class(config)
        model.to(torch_device)

        inputs = self._prepare_for_class(inputs_dict, model_class)

        outputs = model(**inputs)
        output = outputs[0]

        encoder_hidden_states = outputs.encoder_hidden_states[0]
        encoder_attentions = outputs.encoder_attentions[0]
        encoder_hidden_states.retain_grad()
        encoder_attentions.retain_grad()

        output.flatten()[0].backward(retain_graph=True)

        self.assertIsNotNone(encoder_hidden_states.grad)
        self.assertIsNotNone(encoder_attentions.grad)

    def test_generate_with_head_masking(self):
        """Generating with head_masking has not been implemented for ProphetNet models yet."""
        pass


@require_torch
class ProphetNetStandaloneDecoderModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
    all_model_classes = (ProphetNetDecoder, ProphetNetForCausalLM) if is_torch_available() else ()
    all_generative_model_classes = (ProphetNetForCausalLM,) if is_torch_available() else ()
    test_pruning = False
    test_torchscript = False
    test_resize_embeddings = False
    is_encoder_decoder = False

    def setUp(self):
        self.model_tester = ProphetNetStandaloneDecoderModelTester(self, is_training=False)
        self.config_tester = ConfigTester(self, config_class=ProphetNetConfig)

    def test_config(self):
        self.config_tester.run_common_tests()

    def test_decoder_model_past(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_decoder_model_past(*config_and_inputs)

    def test_decoder_model_attn_mask_past(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_decoder_model_attention_mask_past(*config_and_inputs)

    def test_retain_grad_hidden_states_attentions(self):
        # decoder cannot keep gradients
        return


@require_torch
class ProphetNetStandaloneEncoderModelTest(ModelTesterMixin, unittest.TestCase):
    all_model_classes = (ProphetNetEncoder,) if is_torch_available() else ()
    test_pruning = False
    test_torchscript = False
    test_resize_embeddings = False
    is_encoder_decoder = False

    def setUp(self):
        self.model_tester = ProphetNetStandaloneEncoderModelTester(self, is_training=False)
        self.config_tester = ConfigTester(self, config_class=ProphetNetConfig)

    def test_config(self):
        self.config_tester.run_common_tests()


@require_torch
class ProphetNetModelIntegrationTest(unittest.TestCase):
    @slow
    def test_pretrained_checkpoint_hidden_states(self):
        model = ProphetNetForConditionalGeneration.from_pretrained("microsoft/prophetnet-large-uncased")
        model.to(torch_device)

        # encoder-decoder outputs
        encoder_ids = torch.tensor(
            [
                [
                    2871,
                    102,
                    2048,
                    3176,
                    2780,
                    1997,
                    2871,
                    26727,
                    2169,
                    2097,
                    12673,
                    1996,
                    8457,
                    2006,
                    2049,
                    8240,
                    2859,
                    2799,
                    1012,
                    2023,
                    6512,
                    2038,
                    2174,
                    13977,
                    2195,
                    25962,
                    1012,
                    102,
                ]
            ]
        ).to(torch_device)

        decoder_prev_ids = torch.tensor([[102, 2129, 2116, 2372, 2024, 2006, 2169, 1997, 2122, 2048, 2780, 1029]]).to(
            torch_device
        )
        output = model(
            input_ids=encoder_ids,
            attention_mask=None,
            encoder_outputs=None,
            decoder_input_ids=decoder_prev_ids,
        )
        output_predited_logits = output[0]
        expected_shape = torch.Size((1, 12, 30522))
        self.assertEqual(output_predited_logits.shape, expected_shape)
        expected_slice = torch.tensor(
            [[[-7.6213, -7.9008, -7.9979], [-7.6834, -7.8467, -8.2187], [-7.5326, -7.4762, -8.1914]]]
        ).to(torch_device)
        #        self.assertTrue(torch.allclose(output_predited_logits[:, :3, :3], expected_slice, atol=1e-4))
        assert torch.allclose(output_predited_logits[:, :3, :3], expected_slice, atol=1e-4)

        # encoder outputs
        encoder_outputs = model.prophetnet.encoder(encoder_ids)[0]
        expected_encoder_outputs_slice = torch.tensor(
            [[[-0.2526, -0.1951, -0.2185], [-0.8923, 0.2992, -0.4623], [-0.4585, 0.0165, -0.6652]]]
        ).to(torch_device)
        expected_shape_encoder = torch.Size((1, 28, 1024))
        self.assertEqual(encoder_outputs.shape, expected_shape_encoder)
        #        self.assertTrue(torch.allclose(encoder_outputs[:, :3, :3], expected_encoder_outputs_slice, atol=1e-4))
        assert torch.allclose(encoder_outputs[:, :3, :3], expected_encoder_outputs_slice, atol=1e-4)

        # decoder outputs
        decoder_outputs = model.prophetnet.decoder(decoder_prev_ids, encoder_hidden_states=encoder_outputs)
        predicting_streams = decoder_outputs[1].view(1, model.config.ngram, 12, -1)
        predicting_streams_logits = model.lm_head(predicting_streams)
        next_first_stream_logits = predicting_streams_logits[:, 0]
        #        self.assertTrue(torch.allclose(next_first_stream_logits[:, :3, :3], expected_slice, atol=1e-4))
        assert torch.allclose(next_first_stream_logits[:, :3, :3], expected_slice, atol=1e-4)

    @slow
    def test_cnndm_inference(self):
        model = ProphetNetForConditionalGeneration.from_pretrained("microsoft/prophetnet-large-uncased-cnndm")
        model.config.max_length = 512
        model.to(torch_device)

        tokenizer = ProphetNetTokenizer.from_pretrained("microsoft/prophetnet-large-uncased-cnndm")

        ARTICLE_TO_SUMMARIZE = "USTC was founded in Beijing by the Chinese Academy of Sciences (CAS) in September 1958. The Director of CAS, Mr. Guo Moruo was appointed the first president of USTC. USTC's founding mission was to develop a high-level science and technology workforce, as deemed critical for development of China's economy, defense, and science and technology education. The establishment was hailed as \"A Major Event in the History of Chinese Education and Science.\" CAS has supported USTC by combining most of its institutes with the departments of the university. USTC is listed in the top 16 national key universities, becoming the youngest national key university.".lower()
        input_ids = tokenizer([ARTICLE_TO_SUMMARIZE], max_length=511, return_tensors="pt").input_ids

        input_ids = input_ids.to(torch_device)

        summary_ids = model.generate(
            input_ids, num_beams=4, length_penalty=1.0, no_repeat_ngram_size=3, early_stopping=True
        )
        EXPECTED_SUMMARIZE_512 = "us ##tc was founded by the chinese academy of sciences ( cas ) in 1958 . [X_SEP] us ##tc is listed in the top 16 national key universities ."
        generated_titles = [
            " ".join(tokenizer.convert_ids_to_tokens(g, skip_special_tokens=True)) for g in summary_ids
        ]
        self.assertListEqual(
            [EXPECTED_SUMMARIZE_512],
            generated_titles,
        )
        input_ids = tokenizer([ARTICLE_TO_SUMMARIZE], max_length=99, return_tensors="pt").input_ids
        input_ids = input_ids.to(torch_device)
        # actually 98 tokens are used. max_length=100 contains bos and eos.
        summary_ids = model.generate(
            input_ids, num_beams=4, length_penalty=1.0, no_repeat_ngram_size=3, early_stopping=True
        )
        EXPECTED_SUMMARIZE_100 = (
            r"us ##tc was founded in beijing by the chinese academy of sciences ( cas ) in 1958 . [X_SEP] us ##tc "
            "'"
            ' s founding mission was to develop a high - level science and technology workforce . [X_SEP] establishment hailed as " a major event in the history of chinese education and science "'
        )
        generated_titles = [
            " ".join(tokenizer.convert_ids_to_tokens(g, skip_special_tokens=True)) for g in summary_ids
        ]
        self.assertListEqual(
            [EXPECTED_SUMMARIZE_100],
            generated_titles,
        )

    @slow
    def test_question_gen_inference(self):
        model = ProphetNetForConditionalGeneration.from_pretrained("microsoft/prophetnet-large-uncased-squad-qg")
        model.to(torch_device)

        tokenizer = ProphetNetTokenizer.from_pretrained("microsoft/prophetnet-large-uncased-squad-qg")

        INPUTS = [
            "Bill Gates [SEP] Microsoft was founded by Bill Gates and Paul Allen on April 4, 1975.",
            "1975 [SEP] Microsoft was founded by Bill Gates and Paul Allen on April 4, 1975.",
            "April 4, 1975 [SEP] Microsoft was founded by Bill Gates and Paul Allen on April 4, 1975.",
        ]

        input_ids = tokenizer(INPUTS, truncation=True, padding=True, return_tensors="pt").input_ids
        input_ids = input_ids.to(torch_device)

        gen_output = model.generate(input_ids, num_beams=5, early_stopping=True)
        generated_questions = tokenizer.batch_decode(gen_output, skip_special_tokens=True)

        EXPECTED_QUESTIONS = [
            "along with paul allen, who founded microsoft?",
            "what year was microsoft founded?",
            "on what date was microsoft founded?",
        ]

        self.assertListEqual(
            EXPECTED_QUESTIONS,
            generated_questions,
        )